Repetitive Motion Estimation Network: Recover cardiac and respiratory signal from thoracic imaging
This work addresses motion tracking for image-guided interventions in medical imaging, offering a domain-specific improvement.
The paper tackled the problem of recovering cardiac and respiratory signals from thoracic imaging without full motion annotations by proposing the Repetitive Motion Estimation Network (RMEN), which decreased QRS peaks detection offsets by 59.3% compared to alternative models.
Tracking organ motion is important in image-guided interventions, but motion annotations are not always easily available. Thus, we propose Repetitive Motion Estimation Network (RMEN) to recover cardiac and respiratory signals. It learns the spatio-temporal repetition patterns, embedding high dimensional motion manifolds to 1D vectors with partial motion phase boundary annotations. Compared with the best alternative models, our proposed RMEN significantly decreased the QRS peaks detection offsets by 59.3%. Results showed that RMEN could handle the irregular cardiac and respiratory motion cases. Repetitive motion patterns learned by RMEN were visualized and indicated in the feature maps.